Chinese municipalities have developed a large stock of capital assets during a period of rapid growth and urbanization, but have yet to modernize asset management practices. Cities face challenges such as premature decline of fixed assets and spiking liabilities related to operating and maintaining assets. This paper evaluates the asset management practices in three selected small cities and towns in China, using a benchmarking assessment tool followed by an in-depth field assessment. The paper finds that overall performance is below half the international benchmark for good practice in all three cities. Management practices are considerably more advanced for land than for buildings and infrastructure. Key deficiencies in data availability and reporting, governance, capacity, and financial management indicate increased risks for local government finance and the delivery of public services. For small cities and towns where public revenues are often uncertain and limited, urban public services will be at risk of deterioration unless good asset management practices are put in place. The paper recommends strategic actions for upper and lower levels of government, to advance local asset management practices and facilitate the reform agenda.

Based on the population change data of 2005–2009, 2010–2014, 2015–2019 and 2005–2019, the shrinking cities in Northeast China are determined to analyze their spatial distribution pattern. And the influencing factors and effects of shrinking cities in Northeast China are explored by using multiple linear regression method and random forest regression method. The results show that: 1) In space, the shrinking cities in Northeast China are mainly distributed in the “land edge” areas represented by Changbai Mountain, Sanjiang Plain, Xiaoxing’an Mountain and Daxing’an Mountain. In terms of time, the contraction center shows an obvious trend of moving northward, while the opposite expansion center shows a trend of moving southward, and the shrinking cities gather further; 2) in the study of influencing factors, the results of multiple linear regression and random forest regression show that socio-economic factors play a major role in the formation of shrinking cities; 3) the precision of random forest regression is higher than that of multiple linear regression. The results show that per capita GDP has the greatest impact on the contraction intensity, followed by the unemployment rate, science and education expenses and the average wage of on-the-job workers. Among the four influencing factors, only the unemployment rate promotes the contraction, and the other three influencing factors inhibit the formation of shrinking cities to various degrees.